bench_sort_duckdb.py

#!/usr/bin/env python3

#   Copyright (c) 2024-2026 Anton Kundenko <singaraiona@gmail.com>
#   All rights reserved.
#
#   Permission is hereby granted, free of charge, to any person obtaining a copy
#   of this software and associated documentation files (the "Software"), to deal
#   in the Software without restriction, including without limitation the rights
#   to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
#   copies of the Software, and to permit persons to whom the Software is
#   furnished to do so, subject to the following conditions:
#
#   The above copyright notice and this permission notice shall be included in all
#   copies or substantial portions of the Software.
#
#   THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
#   IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
#   FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
#   AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
#   LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
#   OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
#   SOFTWARE.

"""Baseline sort benchmark for comparison with Teide on 10M dataset.

Uses CREATE OR REPLACE TABLE to materialize results,
avoiding Python fetchall() overhead for fair comparison with Teide's
in-C materialization.
"""

import duckdb
import time
import os

CSV_PATH = os.path.join(os.path.dirname(__file__),
                        "..", "rayforce-bench", "datasets",
                        "G1_1e7_1e2_0_0", "G1_1e7_1e2_0_0.csv")

N_ITER = 7

QUERIES = {
    "s1": "SELECT * FROM df ORDER BY id1",
    "s2": "SELECT * FROM df ORDER BY id3",
    "s3": "SELECT * FROM df ORDER BY id4",
    "s4": "SELECT * FROM df ORDER BY v3 DESC",
    "s5": "SELECT * FROM df ORDER BY id1, id2",
    "s6": "SELECT * FROM df ORDER BY id1, id2, id3",
}


def run_query(con, label, sql):
    # Warmup
    for _ in range(3):
        con.execute(f"CREATE OR REPLACE TABLE _result AS {sql}")

    times = []
    for _ in range(N_ITER):
        t0 = time.perf_counter()
        con.execute(f"CREATE OR REPLACE TABLE _result AS {sql}")
        times.append(time.perf_counter() - t0)

    elapsed = sorted(times)[len(times) // 2]
    nrows = con.execute("SELECT COUNT(*) FROM _result").fetchone()[0]
    ncols = len(con.execute("SELECT * FROM _result LIMIT 1").description)
    print(f"  {label:12s}  {elapsed*1000:8.1f} ms   {nrows:>10,} rows x {ncols} cols")
    con.execute("DROP TABLE IF EXISTS _result")


def main():
    csv_path = os.path.abspath(CSV_PATH)
    con = duckdb.connect()

    con.execute("SET threads TO 1")

    print(f"Loading {csv_path} ...")
    t0 = time.perf_counter()
    con.execute(f"CREATE TABLE df AS SELECT * FROM read_csv_auto('{csv_path}')")
    load_time = time.perf_counter() - t0

    row_count = con.execute("SELECT COUNT(*) FROM df").fetchone()[0]
    print(f"Loaded: {row_count:,} rows in {load_time*1000:.0f} ms\n")

    print("Baseline sort benchmarks (single-threaded):")
    print(f"  {'Query':12s}  {'Time':>8s}       Result")
    print(f"  {'-'*12}  {'-'*8}  {'-'*20}")

    for label, sql in QUERIES.items():
        run_query(con, label, sql)

    con.execute("RESET threads")
    nthreads = con.execute("SELECT current_setting('threads')").fetchone()[0]
    print(f"\nBaseline sort benchmarks (multi-threaded, {nthreads} threads):")
    print(f"  {'Query':12s}  {'Time':>8s}       Result")
    print(f"  {'-'*12}  {'-'*8}  {'-'*20}")

    for label, sql in QUERIES.items():
        run_query(con, label, sql)

    print("\nDone.")
    con.close()


if __name__ == "__main__":
    main()